File:Brain morphometry image segmentation.png

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Revision as of 07:32, 5 June 2009 by imported>Daniel Mietchen ({{Image_notes_ownwork2 |Description = Image segmentation using a priori information. In the first step, the image intensities of the T1 image (upper left) are used to plot their frequencies in a histogram. Several peaks – corresponding to different image intensities of the tissue classes – can be differentiated. In the next step, Gaussian curves for each tissue class are fitted into the histogram to estimate the probability of a voxel b...)
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Summary

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Image segmentation using a priori information. In the first step, the image intensities of the T1 image (upper left) are used to plot their frequencies in a histogram. Several peaks – corresponding to different image intensities of the tissue classes – can be differentiated. In the next step, Gaussian curves for each tissue class are fitted into the histogram to estimate the probability of a voxel belonging to that tissue class (bottom left). A map for gray matter is shown (upper right) with the estimated probability for two selected locations (red circles). Based solely on a similar image intensity, the cerebral and the extracranial spot exhibit a similar probability for belonging to gray matter. This can be corrected by combining the image intensity-based information with prior information (below), e.g. using a Bayesian approach.
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Daniel Mietchen
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2009
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